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| DOI：10.13522/j.cnki.ggps.20190294 |
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| Optimizing Water Distribution in Canal Networks Using Multi-objective Particle Swarm Optimization Method |
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LI Tongshu, HUANG Rui, SUN Zhipeng, GUO Shanshan, YI Kang, HAN Yu, CHEN Jian
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1. College of Water Resources & Civil Engineering, China Agricultural University, Beijing 100083, China; 2. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;3. College of Engineering, China Agricultural University, Beijing 100083, China
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| Abstract: |
| 【Background】Most irrigation districts use canal network to distribute water and optimizing water distribution process in the network can improve irrigation water use efficiency and reduce water waste.【Objective】The purpose of this paper is to present methods to optimize water distribution in canal network to help achieve multi-scale optimal allocation of water resource in developing water-saving agriculture.【Method】We took steady flow and minimum water loss as the optimization objectives, and used the multi-objective particle swarm optimization (MOPSO) algorithm to establish a multi-objective canal system optimization model. The model was solved by the two-level canal system with Xijun Irrigation District taken as an illustrative example. The results were compared with that obtained from the backtracking search algorithm and vector evaluation genetic algorithm (treated as CK).【Result】The results revealed that the irrigation period obtained from the MOPSO for the canal network was about 11 days less, with the total water being 205.07 million m3. Compared with the CK, the irrigation period calculated by the proposed method in the canal system was 2~4 days less.【Conclusion】The MOPSO is an effective method to calculate water distribution in the canal network. The calculated results matched the measured water distribution well, and it can be used to manage water distribution both spatially and temporally to satisfy the two optimization objectives. |
| Key words: canal system; optimal water scheduling; multi-objective constraints; particle swarm optimization |
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